{"id":102454,"date":"2021-12-03T15:36:38","date_gmt":"2021-12-03T13:36:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij"},"modified":"2021-12-03T15:36:38","modified_gmt":"2021-12-03T13:36:42","slug":"hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","status":"publish","type":"post","link":"https:\/\/prohoster.info\/it\/blog\/news\/hyperstyle-adaptacziya-sistemy-mashinnogo-obucheniya-stylegan-dlya-redaktirovaniya-izobrazhenij","title":{"rendered":"HyperStyle \u2014 adattamento del sistema di apprendimento automatico StyleGAN per la modifica delle immagini","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

Un gruppo di ricercatori dell'Universit\u00e0 di Tel Aviv ha presentato HyperStyle, una versione invertita del sistema di machine learning StyleGAN2 sviluppato da NVIDIA, rielaborato per ricreare le parti mancanti durante la modifica di immagini reali. Il codice \u00e8 scritto in Python utilizzando il framework PyTorch e distribuito con licenza MIT. <\/p>\n

Se StyleGAN consente di sintetizzare nuovi volti realistici, impostando parametri come et\u00e0, sesso, lunghezza dei capelli, tipo di sorriso, forma del naso, colore della pelle, occhiali e angoli della fotografia, HyperStyle offre la possibilit\u00e0 di modificare parametri simili in fotografie gi\u00e0 esistenti, senza alterare i tratti distintivi e mantenendo il riconoscimento del volto originale. Ad esempio, tramite HyperStyle \u00e8 possibile simulare un cambiamento di et\u00e0 di una persona in una fotografia, cambiare pettinatura, aggiungere occhiali, barba o baffi, dare all'immagine l'aspetto di un personaggio animato o di un dipinto, rendere l'espressione facciale triste o felice. Inoltre, il sistema pu\u00f2 essere addestrato non solo per modificare volti umani, ma anche per qualsiasi oggetto, ad esempio per modificare immagini di automobili.

\"HyperStyle<\/center> <\/p>\n

Il metodo proposto mira a risolvere il problema della ricostruzione delle parti mancanti dell'immagine durante la modifica. Nei metodi precedentemente proposti, il compromesso tra ricostruzione e modificabilit\u00e0 veniva affrontato tramite il fine-tuning del generatore di immagini per sostituire parti dell'immagine target nella ricreazione delle aree modificabili originariamente assenti. Uno svantaggio di approcci simili \u00e8 la necessit\u00e0 di un lungo addestramento mirato della rete neurale per ogni immagine. <\/p>\n

Il metodo basato sull'algoritmo StyleGAN consente di utilizzare un modello standard, pre-addestrato su collezioni di immagini generali, per generare elementi appartenenti all'immagine originale con un livello di verosimiglianza paragonabile agli algoritmi che richiedono un addestramento individuale del modello per ogni immagine. Tra i vantaggi del nuovo metodo c'\u00e8 anche la possibilit\u00e0 di modificare le immagini con prestazioni vicine al tempo reale.

\"HyperStyle<\/center> <\/p>\n

Modelli gi\u00e0 addestrati sono disponibili per volti umani, automobili e animali sulla base delle collezioni Flickr-Faces-HQ (FFHQ, 70.000 immagini PNG ad alta qualit\u00e0 di volti umani), Stanford Cars (16.000 immagini di automobili) e AFHQ (fotografie di animali). \u00c8 inoltre fornito un toolkit per addestrare i propri modelli, cos\u00ec come modelli gi\u00e0 addestrati pronti all'uso di codificatori e generatori standard. Ad esempio, sono disponibili generatori per creare immagini in stile Toonify, personaggi Pixar, schizzi e persino per stilizzare come le principesse dei cartoni animati Disney.

\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\"HyperStyle<\/center>
\n
Fonte: opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"

\u0413\u0440\u0443\u043f\u043f\u0430 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u0435\u0439 \u0438\u0437 \u0422\u0435\u043b\u044c-\u0410\u0432\u0438\u0432\u0441\u043a\u043e\u0433\u043e \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442\u0430 \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u0438\u043b\u0430 HyperStyle, \u0438\u043d\u0432\u0435\u0440\u0442\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u044b\u0439 \u0432\u0430\u0440\u0438\u0430\u043d\u0442 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u043c\u043e\u0439 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0435\u0439 NVIDIA \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f StyleGAN2, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043f\u0435\u0440\u0435\u0440\u0430\u0431\u043e\u0442\u0430\u043d \u0434\u043b\u044f \u0432\u043e\u0441\u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f 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